ABSTRACT Juvenile-onset fibromyalgia (JFM) is a debilitating, chronic pain condition affecting adolescents, primarily females, during a critical period for brain development, and that persists into adulthood for the majority of patients. Due to the lack of definite physical or laboratory findings, JFM has been questioned as a clinical entity, and sometimes regarded as merely an expression of anxiety or depression. This leads to poor understanding, stigmatization, and appropriate disease management, underscoring the need for identifying objective pathophysiology. We have previously used machine learning applied to fMRI data to yield multivariate patterns of distributed brain activity that, together, can identify test subjects as adult FM patients vs. healthy adults with high cross-validated accuracy (93%). However, extrapolating adult FM brain abnormalities to JFM is problematic, given the many factors impacting the developing adolescent brain and the clinical differences between adult and juvenile forms of the disease. The goal of this proposal is to identify brain pathophysiology characteristic of JFM during tailored symptom provocation tasks. There is currently a complete lack of research into the brain correlates of pain in children with widespread pain/JFM. This study will lay the foundation for a line of research in understanding the neurophysiologic underpinnings of JFM, discovering whether brain pathophysiology in JFM differs from adult FM, and assessing treatment effects on specific markers of brain pathophysiology. This study is an R01 ancillary study to the NIH/NIAMS-funded trial (R01 AR070474; Kashikar-Zuck), ?Multi-site randomized clinical trial of Fibromyalgia Integrative Training for Juvenile Fibromyalgia (FIT Teens)?. The exceptionally well characterized cohort of JFM patients from the parent trial presents a unique opportunity to study JFM neural correlates. Our time-sensitive study will transform the scientific output of the parent project by identifying neurophysiological correlates of pain, psychological and physical symptoms in this large, representative, extensively-characterized sample of JFM patients before and after treatment. We hypothesize that machine learning applied to fMRI data during tailored symptom-provocation tasks will identify patterns of neural activity predictive of JFM status (vs. healthy), which will correlate with JFM symptom dimensions (pain, non-painful sensory hypersensitivity, fatigue, and depressive symptoms). This ancillary study will utilize the comprehensive psychological and physical functioning profiles already being captured in the parent R01 trial to identify clinically meaningful neurologic measures in JFM and explore the potential for these measures to change with treatment. This line of research has the potential for a profound impact on understanding and identifying JFM pathophysiology and providing neuro-physiologically informed treatment recommendations.